Arrhythmogenic cardiomyopathy (ACM) is characterized by redistribution of junctional proteins, arrhythmias, and progressive myocardial injury. We previously reported that SB216763 (SB2), annotated as a GSK3β inhibitor, reverses disease phenotypes in a zebrafish model of ACM. Here, we show that SB2 prevents myocyte injury and cardiac dysfunction in vivo in two murine models of ACM at baseline and in response to exercise. SB2-treated mice with desmosome mutations showed improvements in ventricular ectopy and myocardial fibrosis/inflammation as compared with vehicle-treated (Veh-treated) mice. GSK3β inhibition improved left ventricle function and survival in sedentary and exercised Dsg2mut/mut mice compared with Veh-treated Dsg2mut/mut mice and normalized intercalated disc (ID) protein distribution in both mutant mice. GSK3β showed diffuse cytoplasmic localization in control myocytes but ID redistribution in ACM mice. Identical GSK3β redistribution is present in ACM patient myocardium but not in normal hearts or other cardiomyopathies. SB2 reduced total GSK3β protein levels but not phosphorylated Ser 9–GSK3β in ACM mice. Constitutively active GSK3β worsens ACM in mutant mice, while GSK3β shRNA silencing in ACM cardiomyocytes prevents abnormal ID protein distribution. These results highlight a central role for GSKβ in the complex phenotype of ACM and provide further evidence that pharmacologic GSKβ inhibition improves cardiomyopathies due to desmosome mutations.

Among current-generation computer-based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within-individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted.

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This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3–6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ‘if-then’ rules.

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